Node-Sensitive Graphs. Because all three citation networks do not possess even an edge feature, we generate one weak topological feature for each edge, by enumerating the numbers of its adjacent edges. In those experiments, we adopt an EGAT model with \(L\)  = 2 and \(K\)  = 8, where \(L\)  and \(K\)  represent the number of the EGAT layers and the attention heads. For simplicity, we use the same numbers of the output features for every EGAT layer, where \(F_H^{\prime }\)  = 8 and \(F_E^{\prime }\)  = 4, for nodes and edges separately. A one-dimensional convolution operation are performed in the merge layer to produce C features (where C is the number of classes), followed by a softmax function. To improve accuracy, some techniques like dropout[1]} and \(L_2\)  regularization are also used in EGATs. All these experiments, but Pubmed, were run on a machine with two GPUs of Geforce RTX 1080 Ti. Because of a larger requirement on video memory, Pubmed was run on Tesla V100 instead.
